Imputation by linear regression, bootstrap method
Imputes univariate missing data using linear regression with bootstrap
mice.impute.norm.boot(y, ry, x, wy = NULL, ...)
y |
Vector to be imputed |
ry |
Logical vector of length |
x |
Numeric design matrix with |
wy |
Logical vector of length |
... |
Other named arguments. |
Draws a bootstrap sample from x[ry,]
and y[ry]
, calculates
regression weights and imputes with normal residuals.
Vector with imputed data, same type as y
, and of length
sum(wy)
Gerko Vink, Stef van Buuren, 2018
Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice
:
Multivariate Imputation by Chained Equations in R
. Journal of
Statistical Software, 45(3), 1-67.
https://www.jstatsoft.org/v45/i03/
Other univariate imputation functions:
mice.impute.cart()
,
mice.impute.lda()
,
mice.impute.logreg.boot()
,
mice.impute.logreg()
,
mice.impute.mean()
,
mice.impute.midastouch()
,
mice.impute.mnar.logreg()
,
mice.impute.norm.nob()
,
mice.impute.norm.predict()
,
mice.impute.norm()
,
mice.impute.pmm()
,
mice.impute.polr()
,
mice.impute.polyreg()
,
mice.impute.quadratic()
,
mice.impute.rf()
,
mice.impute.ri()
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